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The COVID-19 pandemic has highlighted the dire necessity to improve publichealth literacy for societal resilience. YouTube, the largest video-sharingsocial media platform, provides a vast repository of user-generated healthinformation in a multi-media-rich format which may be easier for the public tounderstand and use if major concerns about content quality and accuracy areaddressed. This study develops an automated solution to identify, retrieve andshortlist medically relevant and understandable YouTube videos that domainexperts can subsequently review and recommend for disseminating and educatingthe public on the COVID-19 pandemic and similar public health outbreaks. Ourapproach leverages domain knowledge from human experts and machine learning andnatural language processing methods to provide a scalable, replicable, andgeneralizable approach that can also be applied to enhance the management ofmany health conditions.

YouTube Videos for Public Health Literacy? A Machine Learning Pipeline to Curate Covid-19 Videos